Antiaging Glycopeptide Protects Human Islets Against Tacrolimus-Related Injury and Facilitates Engraftment in Mice
Bibliographic record
Abstract
Clinical islet transplantation has become an established treatment modality for selected patients with type 1 diabetes. However, a large proportion of transplanted islets is lost through multiple factors, including immunosuppressant-related toxicity, often requiring more than one donor to achieve insulin independence. On the basis of the cytoprotective capabilities of antifreeze proteins (AFPs), we hypothesized that supplementation of islets with synthetic AFP analog antiaging glycopeptide (AAGP) would enhance posttransplant engraftment and function and protect against tacrolimus (Tac) toxicity. In vitro and in vivo islet Tac exposure elicited significant but reversible reduction in insulin secretion in both mouse and human islets. Supplementation with AAGP resulted in improvement of islet survival (Tac(+) vs. Tac+AAGP, 31.5% vs. 67.6%, P < 0.01) coupled with better insulin secretion (area under the curve: Tac(+) vs. Tac+AAGP, 7.3 vs. 129.2 mmol/L/60 min, P < 0.001). The addition of AAGP reduced oxidative stress, enhanced insulin exocytosis, improved apoptosis, and improved engraftment in mice by decreasing expression of interleukin (IL)-1β, IL-6, keratinocyte chemokine, and tumor necrosis factor-α. Finally, transplant efficacy was superior in the Tac+AAGP group and was similar to islets not exposed to Tac, despite receiving continuous treatment for a limited time. Thus, supplementation with AAGP during culture improves islet potency and attenuates long-term Tac-induced graft dysfunction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".